Predictive model for early detection of type 2 diabetes using patients' clinical symptoms, demographic features, and knowledge of diabetes

Abstract

Background and AimsWith the global rise in type 2 diabetes, predictive modeling has become crucial for early detection, particularly in populations with low routine medical checkup profiles. This study aimed to develop a predictive model for type 2 diabetes using health check‐up data focusing on clinical details, demographic features, biochemical markers, and diabetes knowledge.MethodsData from 444 Nigerian patients were collected and analysed. We used 80% of this data set for training, and the remaining 20% for testing. Multivariable penalized logistic regression was employed to predict the disease onset, incorporating waist‐hip ratio (WHR), triglycerides (TG), catalase, and atherogenic indices of plasma (AIP).ResultsThe predictive model demonstrated high accuracy, with an area under the curve of 99% (95% CI = 97%–100%) for the training set and 94% (95% CI = 89%–99%) for the test set. Notably, an increase in WHR (adjusted odds ratio [AOR] = 70.35; 95% CI = 10.04–493.1, p‐value < 0.001) and elevated AIP (AOR = 4.55; 95% CI = 1.48–13.95, p‐value = 0.008) levels were significantly associated with a higher risk of type 2 diabetes, while higher catalase levels (AOR = 0.33; 95% CI = 0.22–0.49, p < 0.001) correlated with a decreased risk. In contrast, TG levels (AOR = 1.04; 95% CI = 0.40–2.71, p‐value = 0.94) were not associated with the disease.ConclusionThis study emphasizes the importance of using distinct clinical and biochemical markers for early type 2 diabetes detection in Nigeria, reflecting global trends in diabetes modeling, and highlighting the need for context‐specific methods. The development of a web application based on these results aims to facilitate the early identification of individuals at risk, potentially reducing health complications, and improving diabetes management strategies in diverse settings.